Update README.md

README.md

@@ -45,7 +45,6 @@ A simple and well designed web app to translate between **Arabic** and **English
 
 ### Table Of Contents
 -  [Introduction](#introduction)
--  [Model Architecture](#model-architecture)
 -  [Project Architecture](#project-architecture)
 -  [How To Run](#how-to-run)
 -  [License](#license)
@@ -53,62 +52,31 @@ A simple and well designed web app to translate between **Arabic** and **English
 
 
 ### Introduction
-A simple and well designed web app to identify the emblem of the top 5 teams of **EPL**. This model has been trained with a balanced dataset which contains almost **5k** images of the emblems of the teams.
-
-
-### Model Architecture
-The model utilizes a straightforward convolutional neural network (CNN) architecture, comprising the following layers:
-
-1. **Convolutional Layer:**
-   - 32 filters, each of size 3x3
-   - ReLU activation function
-   - Input shape: 224x224x3 (RGB images)
-   - Extracts spatial features from input images.
-
-2. **Max Pooling Layer:**
-   - Pool size: 2x2
-   - Reduces spatial dimensions for capturing more global features.
-
-3. **Flattening Layer:**
-   - Flattens the 2D feature maps into a 1D vector for input to dense layers.
-
-4. **Dense Layer 1:**
-   - 64 neurons
-   - ReLU activation function
-
-5. **Output Layer (Dense Layer 2):**
-   - 5 neurons (matching the number of classes)
-   - Softmax activation to produce probability scores for each class.
-
-**Key Points:**
-- Input image size: 224x224 pixels
-- Optimizer: Adam with a learning rate of 0.001
-- Loss function: Categorical crossentropy
-- Performance metric: Accuracy
-
-**Visual Representation:**
-[Input image (224x224x3)] --> [Conv2D] --> [MaxPooling2D] --> [Flatten] --> [Dense 1] --> [Output Layer (Dense 2)] --> [Predicted class]
+A simple and well designed web app to translate between **Arabic** and **English**.
 
 
 ### Prject Architecture
 ```
 ├──  data
-│    └── arsenal  - images of arsenal's emblem.
-│    └── chelsea  - images of chelsea's emblem.
-│    └── liverpool  - images of liverpool's emblem.
-│    └── manchester-city  - images of manchester-city's emblem.
-│    └── manchester-united  - images of united's emblem.
+│    └── arabic2english.txt  - text dataset.
+│
+│
+├──  docs 
+│    └── arabic2english.pdf  - paper of the related research.
 │
 │
-├──  model  
-│    └── football_logo_model.h5  - generated model.
+├──  models  
+│    └── arabic2english.pt  - generated model.
 │ 
 │
 ├──  src
-│    └── classify
-│        └── classify.py  - this module classifies the emblem from input image.
+│    └── data_processing
+│        └── data_processing.py  - this module preprocesses the input data.
 │    └── train
-│        └── trainer.py  - this module trains the model.
+│        └── train.py  - this module trains and saves the model.
+│        └── transformer.py  - model file.
+│    └── translation
+│        └── translate.py  - this module translates the input sentence.
 │
 │
 ├──  app.py - this module starts the app interface.
@@ -153,4 +121,4 @@ Distributed under the MIT License. See `LICENSE` for more information.
 ### Contributor
 Alif Al Hasan - [@alifalhasan](https://huggingface.co/alifalhasan) - [email protected]
 
-Project Link: [https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier](https://huggingface.co/spaces/alifalhasan/epl-top5-emblem-classifier)
+Project Link: [https://huggingface.co/spaces/alifalhasan/arabic2english](https://huggingface.co/spaces/alifalhasan/arabic2english)